[
  {
    "name": "Revenue Autopilot Loop",
    "goal_focus": "marketing revenue generation",
    "summary": "Continuous perception-reasoning-action-reflection loop that keeps the ANI executing, measuring, and improving revenue plays without human intervention.",
    "trigger_keywords": [
      "autonomous",
      "agent",
      "loop",
      "workflow",
      "automation",
      "self-correct"
    ],
    "stages": [
      {
        "stage": "perception",
        "objective": "Ingest the latest campaign telemetry, market data, and prompt context.",
        "actions": [
          "Pull diagnostic signals from Google Ads, Stripe, CJ, Amazon, and statistical stores.",
          "Refresh knowledge caches and feature stores tied to the active campaigns.",
          "Summarize anomalies or gaps detected by monitoring dashboards."
        ],
        "tools": [
          "LangChain data loaders",
          "Feature store APIs",
          "Vector database similarity search"
        ],
        "signals": [
          "roi_trend",
          "ctr_variance",
          "inventory_health",
          "payout_latency"
        ],
        "outputs": [
          "context_snapshot",
          "risk_flags"
        ],
        "metrics": [
          "data_freshness_minutes",
          "signal_coverage"
        ]
      },
      {
        "stage": "reasoning",
        "objective": "Synthesize strategy across RAG, KAG, and statistical reasoning to decide the next action.",
        "actions": [
          "Decompose the user brief into sub-goals using planning prompts.",
          "Score candidate plays with mixture-of-experts confidence weights.",
          "Select the optimal directives balancing revenue upside and risk."
        ],
        "tools": [
          "Planning LLM",
          "Knowledge graph reasoning",
          "Mixture-of-experts router"
        ],
        "signals": [
          "success_probability",
          "graph_consistency",
          "expert_agreement"
        ],
        "outputs": [
          "execution_plan",
          "rationale"
        ],
        "metrics": [
          "plan_confidence",
          "reasoning_latency"
        ]
      },
      {
        "stage": "action",
        "objective": "Execute the plan across marketing systems and marketplaces.",
        "actions": [
          "Trigger Google Ads or CJ automations based on selected directives.",
          "Launch pricing experiments and checkout updates via Stripe and PayPal SDK helpers.",
          "Update Amazon/eBay listings and influencer outreach tasks automatically."
        ],
        "tools": [
          "Process orchestrator",
          "Zapier/Step Functions",
          "SDK clients"
        ],
        "signals": [
          "execution_success",
          "api_error_rate",
          "deployment_latency"
        ],
        "outputs": [
          "action_log",
          "deployment_receipts"
        ],
        "metrics": [
          "tasks_completed",
          "automation_success_rate"
        ]
      },
      {
        "stage": "reflection",
        "objective": "Evaluate outcomes, update memory, and adjust the next loop.",
        "actions": [
          "Compare realized revenue vs. projections and flag deviations.",
          "Write learnings into the performance memory dataset and statistical store.",
          "Adjust knowledge caches and agent routing weights based on feedback."
        ],
        "tools": [
          "Analytics warehouse",
          "Statistical reasoning store",
          "Prompt log cache"
        ],
        "signals": [
          "revenue_delta",
          "confidence_shift",
          "coverage_gap"
        ],
        "outputs": [
          "updated_memory",
          "improvement_backlog"
        ],
        "metrics": [
          "learning_cadence_hours",
          "closed_loop_accuracy"
        ]
      }
    ],
    "guardrails": [
      "Require human approval for spend surges beyond 30% week-over-week.",
      "Pause automation if payout latency breaches SLA thresholds.",
      "Escalate to operators when knowledge coverage drops below 80%."
    ],
    "source": "agentic_loop_dataset"
  }
]